Large language models (LMs) such as OpenAI's GPT-3 [1]}, Google's Meena [2]} and PaLM [3]} and Microsoft's Turing NLGhttps://msturing.org/ have recently captured the interest of the general public through their ability to generate text that is sometimes astonishingly difficult to distinguish from text written by humans. While these models are useful for building open-domain dialog agents, they also have the potential to solve specific NLP problems when provided with an appropriate preamble (LM history) [4]}. We expect general dialog agents to understand the nuances of language such as grammar, conciseness, etc. Thus, we explored using the large LM LaMDA [5]} with a zero-shot preamble that steers the model towards making a sentence more concise. We use the following template to provide the LM context:
